Friction induced hunting limit cycles : a comparison between the LuGre and switch friction model

In this paper, friction induced limit cycles are predicted for a simple motion system consisting of a motor-driven inertia subjected to friction and a PID-controlled regulator task. The two friction models used, i.e., (i) the dynamic LuGre friction model and (ii) the static switch friction model, are compared with respect to the so-called hunting phenomenon. Analysis tools originating from the field of nonlinear dynamics will be used to investigate the friction induced limit cycles. For a varying controller gain, stable and unstable periodic solutions are computed numerically which, together with the stability analysis of the closed-loop equilibrium points, result in a bifurcation diagram. Bifurcation analysis for both friction models indicates the disappearance of the hunting behavior for controller gains larger than the gain corresponding to the cyclic fold bifurcation point

Frequency domain identification of dynamic friction model parameters

This paper presents a frequency domain identification of dynamic model parameters for frictional presliding behavior. The identification procedure for the dynamic model parameters, i.e., (1) the stiffness and (2) the damping of the presliding phenomenon, is reduced from performing several dedicated experiments to one experiment where the system is excited with random noise and the frequency response function (FRF) of the phenomenon is measured. Time domain validation experiments on a servomechanism show accurate estimates of the dynamic model parameters for the linearized presliding behavior

Modeling and identification for high-performance robot control : an RRR-robotic arm case study

We explain a procedure for getting models of robot kinematics and dynamics that are appropriate for robot control design. The procedure consists of the following steps: (i) derivation of robot kinematic and dynamic models and establishing correctness of their structures; (ii) experimental estimation of the model parameters; (iii) model validation; (iv) identification of the remaining robot dynamics, not covered with the derived model. We give particular attention to the design of identification experiments and to on-line reconstruction of state coordinates, as these strongly influence the quality of the estimation process. The importance of correct friction modeling and the estimation of friction parameters are illuminated. The models of robot kinematics and dynamics can be used in model-based nonlinear control. The remaining dynamics cannot be ignored if high performance robot operation with adequate robustness is required. The complete procedure is demonstrated for a direct-drive robotic arm with three rotational joints

Modeling and identification of an RRR-robot

A dynamic model of a robot with 3 rotational degrees of freedom is derived in closed form. A systematic procedure for estimation of model dynamic parameters is suggested. It consists of the following steps: (i) identification of friction model parameters for each joint; (ii) calculation of optimal exciting trajectories, required for estimation of the remaining dynamic model parameters; (iii) estimation of these parameters using a least-squares method. The estimated model satisfactory reconstructs experimental control signals, justifying its use in model-based nonlinear control

Grey-box modeling of friction:an experimental case-study

\u3cp\u3eGrey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.\u3c/p\u3